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TURUN YLIOPISTON JULKAISUJA

ANNALES UNIVERSITATIS TURKUENSIS

SARJA - SER. B OSA - TOM. 287

HUMANIORA

TURUN YLIOPISTO

Turku 2005

LEARNING OF QUANTITATIVERESEARCH METHODS

University Students Views, Motivation,and Difficulties in Learning

by

Mari Murtonen

Mari Murtonen - Learning of Quantitative Research Methods - University Students Views, Motivation, andDifficulties in Learning


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From the Faculty of EducationUniversity of Turku

Supervisors:

Professor Erno LehtinenDepartment of Teacher EducationUniversity of Turku, Finland

Professor Erkki OlkinuoraDepartment of EducationUniversity of Turku, Finland

Reviewers:

Professor, Director Sari Lindblom-YlnneCentre for Research and Development of Higher EducationDepartment of EducationUniversity of Helsinki, Finland

Professor, Director Jan H.F. MeyerCentre for Learning, Teaching, and Research in Higher EducationSchool of EducationUniversity of Durham, United Kingdom

Opponent:

Professor, Director Sari Lindblom-YlnneCentre for Research and Development of Higher EducationDepartment of EducationUniversity of Helsinki, Finland

Cover art:

Suvi KivelKyynelettmt askareet, 2001

ISBN 951-29-2974-0ISSN 0082-6987Painosalama Oy Turku, Finland 2005


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MURTONEN, MARI: Learning of quantitative research methods - University studentsviews, motivation and difficulties in learning

Abstract The aim of this dissertation was to study the difficulties that some students of education,psychology and social science experience in their quantitative research courses at university. Theproblem is approached from the perspectives of anxiety studies, studies on conceptions and beliefs,orientations in learning situations and theories of conceptual change.

In Study I, it was found that research, especially quantitative methods and statistics, appeared tobe more difficult for education and sociology students to learn than other academic subjects, forexample their major subject studies and language studies. The students reported difficulties withsuperficial teaching, linking theory with practice, unfamiliarity with and difficulty of concepts andcontent, constituting an integrated picture of the parts of scientific research in order to reallyunderstand it, and negative attitude toward these studies

By selecting less and more advanced students with the questionnaires developed on the basis ofStudy I, it was found in Case Study II that anxious students concept map of research, drawn in aninterview situation, was more fragmented than the concept maps of less anxious students andexperts. On the basis of Case Study II, it could be hypothesised that difficulties experienced areconnected to students content knowledge.

It is often assumed that the difficulties experienced in the learning of quantitative methods andstatistics could reflect earlier bad experiences with learning of mathematics. Study III revealed thatthe high school mathematics grade was only partly associated with difficulties experienced. A beliefin ones low ability in mathematical subjects was connected to other difficulties experienced in thelearning of research, so there is a mathematical factor involved in difficulties in learning ofquantitative methods. Difficulties experienced were not related to success in university statistics or

research courses, as has also been shown in previous studies.In Study IV, different views on research methods were found in Finland and USA with regardto students appreciation of quantitative, qualitative, empirical and theoretical methods. Studentscould be said to have different research orientations toward methods, meaning a combination ofappreciations of, and readiness to use certain methods. Some of the students had a dichotic attitudetoward quantitative and qualitative methods; they seemed to choose their side between thesemethods. In both countries, a negative research orientation toward quantitative methods was found

which was associated with a positive view on qualitative methods. This qualitative researchorientation was connected in some Finnish students with difficulties in learning of quantitativemethods. When asked about difficulties experienced in learning of quantitative methods, 58% ofthe Finnish students and 21% of the US students reported such difficulties

Study V looked at students views on the need for research skills in their future working life incomparison to their motivational and learning orientations and difficulties experienced in learning

of quantitative methods. It was found that in both Finland and the U.S.A., the students who were

not convinced that they would need research skills in their future work, were less task- and deep-

oriented in their study situations, and experienced more problems with learning than the students

who agreed that they would need research skills. Together, these five studies showed that students difficulties experienced in quantitative

methods courses, research orientations and motivational factors, do constitute an interconnectedweb that may also have implications for content learning and to students views of the importanceof research skills for their future work.

Keywords: Learning of research methodology, learning of quantitative methods, statistics anxiety

UNIVERSITY OF TURKU, Faculty of Education, dissertation, 132 p., December 2005


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joulukuu 2005

MURTONEN, MARI: Kvantitatiivisia tutkimusmenetelmi oppimassa - Yliopisto-opiskelijoiden nkemykset, motivaatio ja vaikeudet oppimisessa

TiivistelmTss vitskirjassa tutkittiin kasvatustieteen, psykologian ja sosiaalitieteiden opiskelijoiden kokemia vaikeuksia yliopistojen kvantitatiivisten menetelmien kursseilla. Ongelmaa lhestyttiintilastopelkojen, tutkimusksitysten ja -uskomusten, oppimisorientaatioiden ja ksitteellisenmuutoksen tutkimusten nkkulmista.

Tutkimuksessa I havaittiin, ett kasvatustieteen ja sosiologian opiskelijat kokivattutkimustaitojen opinnot, erityisesti kvantitatiiviset menetelmt ja tilastotieteen, vaikeampina kuinesimerkiksi paineopinnot ja kieliopinnot. Ongelmina koettiin opetuksen pinnallisuus, teorian jakytnnn yhdistminen, ksitteiden ja sisltjen vieraus ja vaikeus, tutkimuksen kokonaisuudenhahmotus, sek oma negatiivinen asenne.

Tapaustutkimuksessa II selvitettiin parihaastattelulla opiskelijoiden kokemien vaikeuksienmahdollista yhteytt sisltoppimiseen. Haastattelutilanteessa aiheesta tutkimus piirrettyjenksitekarttojen perusteella kahden vaikeuksia kokeneen noviisiopiskelijan ksitys tutkimuksesta olipaljon hajanaisempi kuin kahden pidemmlle edenneen opiskelijan ja kahden ammattitutkijan.

Tilastopelkojen ja tutkimuskursseilla koettujen vaikeuksien selittjksi ehdotetaan usein huonojakokemuksia aiemmissa matematiikan opinnoissa. Tutkimuksen III perustella lukion matematiikannumero oli vain osittain yhteydess vaikeuden kokemuksiin yliopiston tutkimusmenetel-mkursseilla. Heikko usko omiin kykyihin matemaattisten aineiden oppijana oli yhteydess muihinkoettuihin vaikeuksiin tutkimustaitojen oppimisessa, joten matemaattisen osatekij on lsn

vaikeuksien kokemuksessa. Koetut vaikeudet eivt olleet yhteydess kurssiarvosanoihin yliopistossa. Tutkimuksessa IV selvitettiin suomalaisten ja yhdysvaltalaisten opiskelijoiden nkemyksi

kvantitatiivisista, kvalitatiivisista, empiirisist ja teoreettisista tutkimusmenetelmist, sek valmiuksiakytt menetelmi ja niiden oppimisessa koettuja vaikeuksia. Opiskelijoilta lydettiin erilaisiatutkimusorientaatioita, eli menetelmien arvostusten ja kyttvalmiuksien yhdistelmi. Molemmistamaista lytyi ryhm opiskelijoita, joilla oli heikko arvostus kvantitatiivisia menetelmi kohtaan jamatala valmius kytt niit. Tm oli yhteydess korkeaan kvalitatiivisten menetelmienarvostukseen ja valmiuteen kytt niit. Nill opiskelijoilla voidaan sanoa olleen kvalitatiivinentutkimusorientaatio. Osalla opiskelijoista se oli yhteydess vaikeuksiin kvantitatiivisten menetelmienoppimisessa. Suomalaisista 58% ja yhdysvaltalaisista 21% raportoi vaikeuksia tutkimusmenetelmienopinnoissa.

Tutkimuksessa V kysyttiin suomalaisten ja yhdysvaltalaisten opiskelijoiden nkemyksi tutki-mustaitojen tarpeesta heidn tulevassa tyelmssn. Noin puolet opiskelijoista oli epvarma

niden taitojen tarpeesta. Nm opiskelijat kokivat enemmn vaikeuksia tutkimusopinnoissa ja heeivt olleet niin tehtv- ja syvorientoituneita opiskelutilanteissa kuin ne opiskelijat, jotka uskoivattutkimustaitoja tarvittavan tyelmss.

Nm viisi tutkimusta osoittavat, ett opiskelijoiden tutkimusmenetelmkursseilla koetutvaikeudet, tutkimusorientaatiot ja motivationaaliset tekijt ovat yhteydess toisiinsa ja mys heidnnkemyksiins tutkimusmenetelmien tarpeesta tulevassa tyssn.

Asiasanat: Tutkimusmetodologian oppiminen, kvantitatiivisten menetelmien oppiminen, tilastopelot

TURUN YLIOPISTO, Kasvatustieteiden tiedekunta, tutkielma, 132 s., kasvatustiede,


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CONTENTS

CONTENTS............................................................................................................. 5

LIST OF ORIGINAL PUBLICATIONS................................................................. 6

ACKNOWLEDGEMENTS...................................................................................... 7

1. INTRODUCTION .........................................................................................101.1. Emotional and motivational factors in the learning of quantitative research methods.. 12

Anxiety about statistics and research....................................................................13Motivation in situation and approaches to learning..................................................17

1.2. Views, beliefs and conceptions of research.................................................................19Individuals conceptions of research........................................................................19

Cultural conceptions: The two research paradigms....................................................21

1.3. Cognitive processes in the learning of research..........................................................24Developing conceptual understanding of research......................................................27Category of difficult things- a theory of personal categories........................................30

2. AIMS................................................................................................................34

3. METHODS .....................................................................................................353.1 Participants........................................................................................................................353.2 Materials and procedures................................................................................................353.3 Statistical procedures.......................................................................................................38

4. OVERVIEW OF THE EMPIRICAL STUDIES............................................40

5. MAIN FINDINGS AND DISCUSSION........................................................445.1. Limitations of the study ....................................................................................................465.2 General discussion and challenges for future studies....................................................475.3 Practical implications for instruction ...............................................................................515.4. Epilogue...............................................................................................................................55

REFERENCES .......................................................................................................57

ORIGINAL PUBLICATIONS


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LIST OF ORIGINAL PUBLICATIONS

This dissertation is based on the following original publications, referred to in the text by

their Roman numerals:

I. Murtonen, M., & Lehtinen, E. (2003). Difficulties experienced by education andsociology students in quantitative methods courses. Studies in Higher Education,28(2), 171-185.

II. Murtonen, M., & Merenluoto, K. (2001). Novices and experts knowledge onstatistics and research methodology. Proceedings of the 25th Psychology of MathematicsEducation conference,vol 3, pp. 391-398.

III. Murtonen, M., & Titterton, N. (2004). Earlier mathematics achievement andsuccess in university studies in relation to experienced difficulties in quantitativemethods courses.Nordic Studies in Mathematics Education, 9(4), 3-13.

IV. Murtonen, M. (2005). University students research orientations - Do negativeattitudes exist toward quantitative methods? Scandinavian Journal of EducationalResearch, 49(3), 263-280.

V. Murtonen, M., Olkinuora, E., Tynjl, P., & Lehtinen, E. (Submitted). Do I needresearch skills in working life? Students motivation and difficultiesexperienced in quantitative methods courses.


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ACKNOWLEDGEMENTS

If I think of research as a journey, conducting this dissertation research has been awonderful and challenging experience. During my masters studies, I became interested in

the question of why some things are more difficult to learn than others. I noticed manyfellow students saying, and maybe also thought so myself, that quantitative methods areamong those things that are hard to learn. When Professor Erno Lehtinen offered me aresearch topic for my masters thesis about the learning of research, I was immediatelyinterested in it. It sounded challenging, but fascinating. And that is what it still is for me.

I owe my deepest gratitude to my supervisor, Professor Erno Lehtinen, the bestsupervisor a PhD student can have. Erno, your open-minded and extraordinarily positiveattitude has been very important in my academic development. You have always listenedand given an encouraging response to my research ideas and problems, and to myphilosophical considerations. You have taught me by your own expert model how toconduct research, for example, when I have been sitting next to you when you have been

writing an article. You have a special skill of gathering wonderful researchers around youthat has created an excellent environment for learning. Most important, you have taughtme what a scientific community is. Im glad that I have had the opportunity to be one ofthose privileged to work with you.

My second supervisor, Professor Erkki Olkinuora has always helped me when I haveasked for it. Im grateful for his careful comments on my writings and for discussing withme many central questions about measuring motivation. Im impressed by the everlasting,enthusiastic attitude that he has for research. I also appreciate that he has every now and

then taken the time and told me stories about events that happened before the 90s in thefield on research of learning. That kind of information had brought insight to myknowledge about the history of educational science.

Professor Erno Lehtinen has encouraged and supported me to take part in conferencesfrom the very beginning of my doctoral studies. In the EARLI conference in 1999 inGteborg, I had a poster on the theme of Study IV in the present dissertation. In theposter session, a little envelope was attached below each poster. At the end of the day, Ifound a note in my envelope saying, Please, contact me. Erik Meyer. Since then I havehad the opportunity to learn about his interesting studies on conceptions of research. I amgrateful to him for introducing me to a great research group, the SCoRI (Students'

Conceptions of Research Inventory) group, including Margaret Kiley, and Gerry andHelen Mullins, who invited me to a seminar in Australia and introduced me to thebeautiful city of Adelaide. I am also very grateful for Professor Meyers careful commentson the manuscript of my doctoral dissertation.

When I was finishing my masters education, I studied cognitive science in Helsinkiand worked in the Pedagogical Unit of the Faculty of Medicine at the University ofHelsinki. That was where I first met Professor Sari Lindblom-Ylnne, the other examinerof my doctoral dissertation. I have learnt to know Sari as a charming and dynamic person

who always sees the positive sides of things. Her sharp comments on my dissertation

manuscript in the sunny yard of the University of Cyprus, at the EARLI 2005 conference,helped me to see how to improve the manuscript.


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The Pedagogical Unit was directed by Professor Kirsti Lonka, to whom I am deeplyindebted for the possibility to work with this innovative and creative research group,

where I also met Annamari Heikkil and Virpi Slotte. I want to thank you all for showingme what an inspiring team a research group can be.

At the time of my masters studies in education, one of my most influential teachers

was Doctor Markku Huttunen. His excellent lectures about philosophy of education andabout scientific thinking gave depth to my studies and challenged me to think. Markkuencouraged me to continue my studies in philosophy, which has since become one of thecentral standpoints of my research.

I have had a great opportunity to work with many interesting research groups andenvironments. At the beginning of my doctoral studies, I worked at the Centre forLearning Research at the University of Turku. The Centre, directed by Professor Marja

Vauras, was where I met my close work colleagues, Sirpa Lehti, Kaarina Merenluoto,Mirjamaija Mikkil-Erdmann and Tuire Palonen, with whom I have shared manyimportant moments of growing to be a member of the research community. I am grateful

to you for the inspiring discussions we have had together about various topics, includingthe teaching and learning of research methods, and for the research and teaching workconducted together, that have taught me a lot.

Since 2001, when the Faculty of Education moved to Educarium, I have been workingat the Department of Education, which is led by Professor Risto Rinne. I want to thank allthe researchers at the department, and also at the faculty, for the pleasant and friendly

working environment. I especially wish to thank Juhani Thtinen, Tuike Iiskala and MattiLappalainen for their co-operation.

During 1999-2000, I visited the University of California at Berkeley, on the invitation

of Professor Andrea diSessa. His inspiring research group, including Nathaniel Titterton,who is a co-writer of one of the articles of my dissertation, taught me a lot about the goodfunctioning of a research group. I want to thank the Finnish Cultural Foundation, the

Academy of Finland, the Faculty of Education, and the University of Turku forsupporting my studies in the USA. I am also grateful to the Turku University Foundationfor the one-year grant to finish my doctoral dissertation, thanks to which I completed the

work.Most of the time during my doctoral studies I have been working in research projects

of the Academy of Finland about development of expertise. This work has been veryfruitful and it has given me new insight into my doctoral dissertation topic. I am very

grateful to Professor Pivi Tynjl for being my mentor in these projects and sharing herbrilliant views about many theoretical and also practical points with me. I am also gratefulto the newest member of our research group, Sari Sahlstrm, whose energeticcontribution has been central to our current project.

Sometimes I have felt quite lonely working on this little studied domain of learning ofresearch. Every now and then, usually in conferences, I have been fortunate to meet otherresearchers of this subject. I want to thank the following researchers for their co-operation: Juhani Rautopuro, Pertti Visnen, Antero Malin, Gunilla Petersson, AngelaBrew, Gina Wisker, Iddo Gal, and Tony Onwuegbuzie.

The data for this doctoral study have been collected from various courses at theUniversity of Turku and the University of California at Berkeley. I want to thank thoseteachers who helped me to collect the data on their courses. I especially wish to thank


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Professor Jack Gallant from Berkeley, who was very supportive. I thank Jacqueline Vlimki and Raija Hammar for helping me with the English in my articles anddissertation, and Eero Laakkonen for statistical support in my work.

I have a great fortune to have many wonderful friends. I thank you all for the thingslived together, and for all the joys and sorrows that we have shared. I especially want to

thank Eeva Salmi, Nina Halttunen, Piia Seppnen and Tiina Erpolku for being such dearfriends. Sharing thoughts and experiences of life with you has been very important for mylife and for giving me energy to write this dissertation.

I want to thank my family, my parents Sinikka and Jorma Murtonen, my brother PauliMurtonen, and my grandparents Maija and Yrj Laaksonen, Uuno Murtonen and BeritEdin for always supporting me in so many ways. Your loving care has given me a safeenvironment to grow up in and your views and thoughts on life have taught me much.Kiitos kaikesta!

I met my husband Pasi in Helsinki where I was studying in 1996. Soon after, when Iwas graduating as a master of education, I told him that I was planning to continue my

studies, and that it would include a year in the USA, possibly in California. I was a little bitworried about telling him this, but what did I see: clear pictures of surfing boards in hiseyes! So, it was agreed, we would travel together. Pasi, you have been there with me andsupporting me for the whole time of my doctoral studies. You are the one who alwayscherishes me and brings new and wonderful things into my life. Another one who bringshappiness into our life is Selma, our two-year-old daughter. I love you two, you are thesunshines of my life!

Tammikalliolla, November 2005,

Mari Murtonen


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Introduction10

1. INTRODUCTIONLearning of research is one of the most important tasks at the university. It is also one ofthe most challenging tasks. Students in many disciplines have reported having problems

with research courses. Quantitative methods and statistics courses in particular have beennoticed to cause problems in many disciplines, such as in education (Lehtinen & Rui,1995; Onwuegbuzie & Daley, 1998), in psychology (Hauff & Fogarty, 1996; Pretorius &Norman, 1992; Thompson, 1994; Townsend et al., 1998), in sociology (Filinson & Niklas,1992), in social work (Epstein, 1987; Forte, 1995; Green et al., 2001; Rosenthal & Wilson,1992), and in social science in general (Zeidner, 1991). Many teachers are aware of theproblem, as Wilson and Rosenthal (1992) write: Social work educators in general, andteachers of research in particular, know from their interactions with students that social

work students are highly anxious about taking research and statistics courses. Theproblem is not new; for example, Linn and Greenwald wrote already in 1974 aboutstudents negative attitudes related to knowledge of research and about problems inmaking research courses relevant to social work students.

The difficulties that students experience in quantitative research courses may result inpoor learning and low course grades, but they may also have wider implications. Students

with difficulties may not be as eager to take voluntary courses in quantitative methods, themethods used in their course work may be restricted by the difficulties, and they may havedifficulties in completing degrees (e.g. Meyer, Shanahan & Laugksch, 2005; Kiley &Mullins, 2005). The difficulties may even be reflected in students views on their future

work and selecting a job (e.g. Onwuegbuzie, 2000). It is also possible that the difficulties

experienced during university studies have an impact on how prepared someone is tocarry out certain tasks when employed and on the quality of the work done.

Although the problem of difficulties experienced in learning of quantitative methods atuniversity is not new, it has been little explored. There are very few empirical studies ingeneral on the learning of research skills. The most active domain of research aboutteaching and learning of quantitative methods has been social work education: the Journalof Social Work Education and the Journal of Teaching in Social Work have mostfrequently published articles on this issue. More research has been published on thedomain of statistics teaching and learning. The interest in publishing on these topics seemsto be growing, as is the interest in conducting empirical studies on the topic. Becker

(1996) has conducted a review of the published literature on statistics teaching. Of the 501references and 29 dissertations identified, only 3% were dated prior to 1970, while nearly athird were published in 1990 - 1995, indicating the growth of interest in this subject. Shefound that the literature was largely anecdotal and mainly comprised recommendations;less than 30% of the literature reported results of empirical studies. The non-empiricalpapers usually provide ideas for developing classroom activities and report the use ofcomputer software.

A more up-to-date bibliography by Hafdahl (2004) on correlates of statistics anxietyshows that of the 538 articles found on the subject, nearly 60% have been published since

1995 and over 30% since 2000. These articles on statistics anxiety have been mostfrequently published in psychological journals (Hafdahl, 2004).


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Introduction 11

The increasing interest in the learning of statistics and research skills in general ismotivated by the development of western society. Research has become very significant inall fields of a knowledge-based society. Philippe Busquin (2001) states, in his preface tothe European Commission publication "Towards a European Research Area", thatresearch and development are seen as a generator of knowledge, growth, employment and

social cohesion. Greer (2000) points out that the amount of information based onresearch and statistical analysis is growing in our society. Technical development and theincreasing amount of information produced and made available by computers require theskills to handle this information in many occupations. Because of the various collectionand analysis methods, the complexity of the information has also substantially increased.

Adequate use of the wealth of information requires that the citizens of a knowledgesociety develop more advanced and complex knowledge-handling skills (e.g. Bereiter &Scardamalia, 1993; Murtonen & Lehtinen, 2005). The ability to understand and make useof research-based information is becoming one of the key competencies of future expertpractices. However, it is not only researchers who are directly dealing with research need

these skills. Experts in many other professions also need skills to understand and evaluateresearch-based information. In Finland, Laukkanen (2001) has emphasised the role ofresearch in policymaking, which includes new and challenging features such as thegrowing complexity of society, globalisation, social displacement and maintaining welfare.

The skills to understand research are thus needed on many levels, even on those thatpreviously were considered to not necessarily need research skills. As Cerrito (1999) putsis, Statistical literacy is no longer a luxury; it is a necessity. Similarly, it could be saidabout research skills that they are no longer a luxury or only needed by researchers, but arerequired in many tasks and occupations.

The goal of research instruction is thus to produce graduates capable of handlingresearch information. Unfortunately, the outcomes of statistics and methodology coursesoften seem to be only the acquisition of a set of isolated facts and skills without a deeperunderstanding of research (e.g. Murtonen, Iiskala, Merenluoto & Thtinen, 2002).Universities are investing considerable resources to teach students research skills, but thelearning outcomes of the methodology courses are often not as good as expected, noteven after several courses (Lehtinen & Rui, 1995; Garfield & Ahlgren, 1988, Rautopuro,

Visnen & Malin, 2004). The research literature also suggests that students difficulties donot decrease during education. On the contrary, attitudes toward research become lesspositive (e.g. Siegel, 1983). There is a need for better approaches to teaching and helping

the students learn scientific thinking and research methods in a more effective and deeperway.

The aim of the present study is to explore the problems in the learning of researchskills at university. Because very little empirical research exists on education and socialscience students difficulties in learning of general research skills and quantitative methods,the present study aims first at exploring the possible difficulties that the students may havein learning of these skills. The focus is especially on learning of quantitative methods,because the earlier research literature, comprising mainly of teachers views andrecommendations for instruction, suggests that this is problematic for many students.

The concepts quantitative research, quantitative methods and statistics (in themeaning of referring to procedures used as tools in empirical research, not as a separatescientific discipline) are all often used when referring to certain type of difficulties in


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Introduction12

learning in the literature concerning the teaching and learning of quantitative research. Thewords research and methodology have been used as top-level concepts to cover bothformer concepts. For example, Epstein (1987) writes about the fear of mathematics andstatistics, and concludes that they are connected to the anti-research attitudes of social

work students. Similarly, in the present study, the terms research and also

methodology are used as top-level concepts that include the narrower quantitativeresearch methods and statistics. It is known on the basis of earlier studies that manysocial science students have anxiety about statistics (e.g. Onwuegbuzie, 2000), but thepresent study was not restricted only to studying the anxiety about statistics, because it

was assumed that the students problems are wider. This means that if they have problemswith statistics, they probably have problems also on the more general level of learning ofquantitative research, and these problems may also influence their other views of research.

Thus, the aim in the present study is to examine the learning of quantitative methods thatincludes both statistics and more general issues of research.

In addition to the exploration of the problems that the students may have with

learning of quantitative research, the goal of the present study is to find out what thosedifficulties may be connected to. Previous bad experiences with mathematics are oftenclaimed to be the reason for anxiety about statistics, and thus also the reason for studentsdislike of quantitative methods. It is also often assumed that problems in learning result inlow course grades. However, majority of the students pass research courses, but still, manystudents report difficulties. Could there be some other reasons that influence students

views and experiences about research? Could, for example, the paradigmatic division ofsocial and educational sciences into quantitative and qualitative research be connected tohow students orientate themselves to research? In the present study, students views on

quantitative and qualitative methods are examined, as well as their motivational factors instudying quantitative research. The goal of the present study is also to see whetherdifficulties have an impact on students content learning of research, evaluated with otherinstruments than course exams. The goal of teaching of research at university is to preparestudents with research skills to be able to conduct research-related tasks in their future

work. A question for the present study is that do students understand the relevance ofresearch skills for their future work? Further, are their views about work associated withmotivational factors and difficulties in learning?

1.1.

Emotional and motivational factors in the learning of quantitativeresearch methods

"No other part of the social work curriculum has been so consistently received by studentswith as much groaning, moaning, eye-rolling, hyperventilation, and waiver strategizing asthe research course." (Epstein, 1987,71).

Emotional and motivational factors are always present in all learning, but in quantitativemethods and statistics courses at university they are particularly visible. While teachers tryto teach students the contents of the subject area, students having problems with learning

may experience a wide range of emotions that impede learning. No matter how well the


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Introduction 13

teacher has prepared the instruction, there may be no way to get the student toconcentrate on the task if he or she is mainly focusing on coping with negative feelings.

Until the end of the 1980s, research on motivation made little contribution to researchon learning. Problems in behaviour and learning were often seen to be due toinformation-processing errors and cognitive limitations. Motivation and cognition were in

the main studied separately, and the context of learning was not considered. Theinseparability of cognition and motivation became acknowledged by the 1990s, andresearchers also started to pay attention to context. Motivation is no longer a separate

variable or a distinct factor, which can be applied in explanations of an individualreadiness to act or learn but it is reflective of the social and cultural environment.(Jrvel, 2001.)

A similar history can be seen on the area of learning of research and statistics. According to Gal and Ginsburg (1994), while statistics educators have focused onimproving the cognitive side of instruction, i.e. the skills and knowledge that students areexpected to develop, little regard has been given to non-cognitive issues, such as students

feelings, attitudes, beliefs, interest, expectations, and motivations. There is only oneexception to this: studies on anxiety about statistics.

Anxiety about statistics and research

Anxiety about statistics

Negative feelings about statistics are evident in many places, for example, in manytextbooks on statistics. The back cover of Hintons (1995) Statistics Explained, A GuideFor Social Science Students asks Do you hate statistics? Birenbaum and Eylath (1994,

93) found introductory statistics books from the 70s and 80s named Statistics withouttears and Social Statistics without tears, some of them declaring that enjoying statisticsis rather like eating nettles it gives you a reputation of being rather odd. The same typesof titles continue to appear in the 90s, for example, when Forte (1995) published hisarticle with the title Teaching statistics without sadistics.

Statistics anxiety has been characterised by extensive worry, intrusive thoughts, mentaldisorganisation, tension, and psychological arousal that arise in people when exposed tostatistics content, problems, instructional situations or evaluative contexts (Zeidner, 1991).

The questions in statistics-anxiety questionnaires usually concern emotional states, such asfeeling anxious about using statistical tables, reading a formula, or signing up for a

statistics course (e.g. Zeidner, 1991). Statistics anxiety has been shown to be separate fromgeneral test anxiety (Benson, 1989; Benson & Bandalos, 1989), i.e. it is not just the testsituation that explains anxiety about statistics. Wilson and Rosenthal (1992) differentiatestate-anxiety in specific circumstances (e.g. statistics) from trait-anxiety that refers to amore general tendency to be anxious. Trait-anxiety and statistics state-anxiety did notcorrelate with each other in their study, meaning that these two constructs are separate.

Statistics anxiety has been found to be a serious problem in quantitative methods andstatistics courses to many university students, for example in social sciences (e.g.Birenbaum & Eylath, 1994; Forte, 1995; Pretorius & Norman, 1992; Townsend et al.,1998; Zeidner, 1991). In a study by Wilson and Rosenthal (1992), 51% of the socialscience students reported moderate anxiety about research and statistics, while 27%reported high or very high anxiety, and 22% low anxiety. Statistics anxiety has been also


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Introduction14

reported in many other disciplines, such as in biology (Kelly, 1992) and in business(Zanakis & Valenzi, 1997), but it is supposed that especially students in the social sciences,education, psychology and other human sciences express more anxiety aboutmathematical and statistical subjects than, for example, students in the natural sciences(e.g. Forte, 1995). Royse and Rompf (1992) found that undergraduate social work students

experienced more maths anxiety when compared to students in other disciplines.Problems in the learning of statistics are often associated with problems in the learning

of mathematics. There seems to be a connection between the nature of these domains,and both are considered as hard to learn by many. Mathematics and statistics are not,however, identical domains. Bisgaard (1991, 276) describes statistics as the art andscience of collecting and analyzing data. He continues: Like physics, it is a sciencedistinct from mathematics. It is true that statistics, like physics, draws heavily onmathematics for developing theory and methods; I would like to emphasize that weshould not underestimate the importance of mathematics for statistical theory. But asphysics is not just applied differential equations, so is statistics not just applied

probability. Statistics, thus, is an independent discipline, but connected closely tomathematics. Both are often referred to when talking about learning of either. Forexample, in a book on adult numeracy development, edited by Gal (2000b), issuesconcerning both mathematics and statistics are discussed. Similarly, mathematics professorPaulos, in his book Innumeracy (1991), gives examples in the domains of bothmathematics and statistics. Numbers are used in both. For social science, psychology andeducation students, statistics may be connected to mathematics at first glance because ituses the same symbolic language as mathematics, and also because their prior courses instatistics, for example, in high school might have been taught as a part of the mathematics

curriculum.Students do not necessarily experience statistics and mathematics similarly. Forexample, in a study by Merenluoto and Murtonen (2004), students reported experiencingstatistics as uncertain, unstable and detailed, while mathematics was considered as strictand stable. Although students may experience these differences between mathematics andstatistics, their difficulties in these domains may still have a common basis. There might besome common features that evoke the similar type of emotional reactions, such as the useof numbers. According to Gal (2000a), some adults, including highly educated ones,decide that they are not good with numbers. These types of beliefs may hinder thelearning of both mathematics and statistics.

Girls are usually seen as being less interested in technical or hard subjects, such asmathematics and physics (e.g. Hoffmann, 2002; Ntnen, 2000). It has also beenhypothesised that women would experience more statistics and mathematics anxiety thanmen, but the evidence is not unequivocal in the case of university students. The effect ofgender in statistics anxiety has been found to be weak (eg. Benson, 1989). In a study byZeidner (1991) on behavioural science students, females were observed to have higherstatistics test anxiety than males, whereas males were found to have higher statisticscontent anxiety than females. In a recent Finnish study (Soro, 2002), primary schoolteachers still saw girls and boys as different kinds of learners of mathematics. Girls were

considered to be dutiful and good in routine tasks, and boys to be insightful and good indemanding, inferential thinking. It is probable that some university teachers share theseviews. It is often hypothesised that, at university, quantitative methods are hard for social


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science students, because a large part of the students are women. According to Soro(2002, 50), it is possible that by formulating a research question such as why are girls notas good in mathematics as boys, a self-fulfilling prediction can occur. Thus, the questionabout difficulties in social sciences with quantitative methods should be formulated aswhy is it difficult for these students, regardless of gender.

The studies on statistics anxiety have their roots in mathematics anxiety andmathematical beliefs studies. Anxiety about mathematics has been found to begin at anearly age, as over 60% of 9- to 11-year-old pupils reported some degree of mathematicalanxiety in Newsteads study (1998). According to Zeidner (1991), statistics anxietyparalleled some known features of mathematics anxiety in the same behavioural sciencestudent population. Birenbaum and Eylath (1994) explored different correlates of statisticsanxiety among students of the educational sciences and found that mathematics anxietyand statistics anxiety were related.

Negative prior experiences with mathematics, poor prior achievement in mathematicsand a low sense of mathematical self-efficacy have been found to be meaningful

antecedent correlates of statistics anxiety (Zeidner, 1991). Birenbaum and Eylath (1994)found that a low high school mathematics grade was connected to education studentsexperience of anxiety about both mathematics and statistics. Earlier mathematicsachievement thus seems to be related to statistics anxiety. Experiences with statistics donot seem to be as important; Birenbaum and Eylath (1994) studied the impact of previousexperience with statistics on statistics anxiety and concluded that whether or not thestudent had previously taken courses in statistics for behavioural sciences at university, didnot affect statistics anxiety.

Anxiety about research

In the case of learning of research in general, or learning of quantitative methods, almostno research on emotional factors exists. Most of the few research papers just note that theproblem exists, and they usually concentrate on proposing a new way of teaching research,or speculate about what contents should be taught (e.g. Epstein, 1987; Filinson & Niklas,1992; Quinn, Jacobsen & LaBarber, 1992; Morris, 1992). However, there are some studieson the role of statistics anxiety in research methodology courses, or on describing anxietyabout research. Wilson and Rosenthal (1992) have studied anxiety about research andstatistics which they conceptualised as a specific state-anxiety that involves negativeemotional reactions, such as tension and nervousness, occurring upon the contemplation

of taking a course in research and statistics. Their method was to ask students to thinkabout taking a course in research and statistics, and to report their feelings about, forexample, comfortable, worried, nervous, calm, relaxed and tense (Wilson and Rosenthal,1992, 78). Their study was thus very similar to statistics anxiety studies, except that theyincluded the word research in their theme of research.

The pioneering work of Onwuegbuzie (1997) studied statistics anxiety (e.g. fear ofstatistics language, fear of application of statistics knowledge), research process anxiety(e.g. fear of research language, fear of application of research knowledge), compositionanxiety in writing (e.g. content anxiety, format and organisational anxiety), and library

anxiety (e.g. perceived library competence, perceived comfort with the library). These allwere found to be connected to students inability to undertake and to write an effectiveresearch proposal in an introductory research methodology course. This research


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proposal writing anxiety thus appears to involve a complex array of emotional reactions which can inhibit the ability to formulate a research problem, to conduct an extensivereview of the literature, to develop a frame of reference, to formulate research questionsand hypotheses, to select a research design, to define the population and sample, todevelop a plan for data collection and analysis, and to write the research proposal. On the

basis of these findings of Onwuegbuzie, we could assume that, in addition to differenttypes of anxieties, difficulties in the learning of research are connected to a wide set ofproblems involving students beliefs, fears, views and experiences.

The problem with anxious students is that they may also have other problemsconnected to their feeling of anxiety, such as failing research courses, achieving lowgrades, procrastination with studies or avoiding statistics and research in their futuredecisions. Studies on children have revealed that pupils sense of their own mathematicalability, their expected mathematical performance and their overall academic performanceall correlate strongly with each other Schoenfeld (1989). The situation is, however, not soclear with university students.

Pretorius and Norman (1992) compared anxious and non-anxious psychology studentson a research methodology course in terms of passing or failing, and found that the mostanxious students did not pass the course. However, a correlation between anxiety andachievement has not been found in many studies involving university students, or it hasbeen weak. A study by Zeidner (1991) on social science and education students suggeststhat there would be a weak correlation between statistics anxiety and statistics courseperformance. Similarly, in a study by Benson (1989), university students statistical testanxiety was found to be weakly connected to achievement. In a study by Wilson andRosenthal (1992), US social work students anxiety about research and statistics was not

related to performance on the foundation research and statistics course. Also in Rosenthaland Wilsons (1992) study on a social work master students research course, it was foundthat confidence in undertaking the research course was not related to performance. In thestudy of Birenbaum and Eylath (1994), neither statistics nor mathematics anxiety wasconnected to the statistics-related course grade.

Students earlier experiences with mathematics tend to explain university statisticscourse grades more than anxiety. Townsend et al. (1998) found that university psychologystudents mathematics backgrounds did become a significant predictor of overallachievement in a statistics course. The students who had taken more mathematics courseshad higher statistics grades than the students with fewer mathematics courses. Although

the number of courses taken was connected to success, earlier achievement level did notseem to be so clearly related to success at university. Birenbaum and Eylath (1994) foundthat the earlier high school mathematics grade was only weakly connected to the statisticscourse grade at university.

In summary, previous research suggests that earlier achievement in mathematics hassome correlation with statistics anxiety at university, and is also weakly correlated withachievement in university statistics and methodology courses. However, there seems notto be always a relationship between statistics anxiety and university research and statisticscourse grades.

Anxiety seems to be a complex concept, and its components appear to be difficult tomeasure. Moreover, the effects of anxiety on other factors like course performance seemto be hard to establish. Whether or not anxiety has an impact on students achievement on


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research courses, anxieties may have other, even more serious, effects on students furtheractions. Anxieties can be very harmful for learning. Onwuegbuzie (1997) found that evenroutine problems like parking at the library could increase research proposal writinganxiety levels significantly. In an anxious state, a person cannot concentrate on a cognitivelearning task as well as in a non-anxious state. According to Onwuegbuzie (1997), statistics

high-anxious students tended to give up research proposal writing more easily than theirlow-anxious counterparts. They also incorrectly believed that they did not have the abilityto learn statistical concepts. Onwuegbuzie also concludes that anxious students tended toengage in procrastination, which is in line with the assumption that problems in thelearning of research would result in difficulties in completing degrees (e.g. Meyer,Shanahan & Laugksch, 2005; Kiley & Mullins, 2005).

In a study by Green, Bretzin, Leininger and Stauffer (2001), it was found that social work students who reported higher levels of anxiety about research tended to be lesspositive about the importance of research to their profession. In a study by Onwuegbuzie(1997), students who displayed the highest levels of statistics anxiety tended to view

statistics as irrelevant for their future development, whether academic or career. Aconclusion may be drawn from these results that high research or statistics anxiety isconnected to not considering research or statistics very important. Thus, it is hypothesisedin the present study that difficulties in the learning of quantitative research are connectedto not considering research skills very important in working life.

Motivation in situation and approaches to learning

In the case of learning of research, motivation has been seen as one of the majorproblems causing difficulties in learning. Students have been seen as underestimating the

value of research skills for their studies and future work, and thus being non-committed tostudy. (E.g. Murtonen et al., 2002; Murtonen, 2004.) In addition, feelings of difficulty andanxiety can be thought of as hindering the motivation to study. Research courses are oftenobligatory for social science students. Thus, they have to take these courses, whether theyare motivated or not.

In learning situations students can focus their attention to the task, or they may havetask-irrelevant behaviour. Lehtinen, Vauras, Salonen, Olkinuora and Kinnunen (1995)have studied pupils situational orientations. These situational orientations are concerned

with the target of the students focus at some specific moment. When given a task, some

people start to solve the given task, i.e. focus on the task, while others are more interested,for example, in how to please the teacher or just get themselves out of the problem-solving situation. Situational orientations to learning in specific situations have beenmostly researched with children. Olkinuora and Salonen (1992) have found that childrendo have situational orientations to learning that may not foster learning. Some students arenot task-oriented, but instead they have an ego-defensive, or a socially motivatedorientation, that draws their cognitive activities away from the task. Ego-defensiveorientation means that a student is most concerned about the coping of the self whengiven a task, and her or his self-efficacy is low. The socially oriented student uses her/hisenergy to please the teacher and does not really try to solve the task. The task-oriented

person is eager to solve the task and does not give up even if the solution does not comeeasily. (Olkinuora & Salonen, 1992; Salonen, Lehtinen & Olkinuora, 1988.) Situational


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orientations have been seen as established gradually through childrens learning and socialreward or control histories in family and school contexts (Vauras, Salonen, Lehtinen &Lepola, 1999). Situational orientations to learning among university students have beenstudied in general learning situations by Mkinen and Olkinuora (2004). They found thattask-orientation and performance orientation were connected to a meaning-oriented and

self-regulated learning strategy, while avoiders and socially-oriented students mostfrequently reported the use of a reproduction-oriented and externally regulated strategy.

Eronen, Nurmi and Salmela-Aro (1998) studied university students achievementstrategies in study situations. They identified four types of strategies: optimistic, defensive-pessimistic, impulsive and self-handicapping. These categories share similarities with theclassification of Olkinuora and Salonen (1992) of situational orientations, such as self-handicapping and ego-defensiveness, both of which are associated with potential failureand which may thus lead students to concentrate on task-irrelevant behaviour. Students

who are ego-defensive may self-handicap themselves, for example, by giving up orclaiming that the task is not important, rather than taking the risk of failing to solve the

problem. According to Thompson and Richardson (2001), the benefit of self-handicapping is in sparing individuals from conclusions about their low ability by blurringthe link between ability and performance. University research course students may behaveego-defensively, for example, by saying that research skills are not important, thus aimingat avoiding the possibility of working hard for a research course only to achieve a lowgrade.

In addition to students motivational orientations in learning situations, theirapproaches to learning have an effect on the quality of their learning. Studentsapproached to learning have been found to be mainly deep, surface or strategic; deep

approach refers to understanding, surface approach refers to reproducing, and strategicapproach refers to achievement or time-management goals (e.g. Marton & Slj, 1976,Entwistle & Ramsden, 1983). According to Lindblom-Ylnne and Lonka (1999), studentsconceptions of learning, approaches to learning and their level of processing may beroughly divided into two categories: surface-level reproduction (or memorizing) versusdeep-level transformation (or construction) of knowledge, the latter being associated withqualitatively better learning outcomes.

While approaches to learning are found to be deep or surface (Marton & Slj, 1976),more general ways to orient oneself towards learning have been called learningorientations. According to Vermunt (1996), learning orientations refer to the whole

domain of personal goals, intentions, attitudes, worries and doubts of students in relationto their studies, and they are supposed to influence learning because students mainly usethe activities they think are best suited to realize their personal goals. A broader stillconcept is study orientation, referring to students general ways to orient themselves tostudying, including their learning approaches and motivational factors (e.g. Entwistle,Meyer & Tait, 1991). Meyer (1991) introduced the term study orchestration to indicatethat the association of constructs that represent approaches to studying at an individuallevel is a context-specific response, and is affected by the qualitative level of perception ofthe individual towards certain key elements of the learning context. The notion of context

specificity is very important in the case of learning of research skills.To conclude, effective learning usually follows from good concentration on the taskand deep approach to learning. Task-oriented learning focuses cognitively on the given


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task: attempts are made to solve the task and the effort of the learner or problem solver isdirected toward the content features of the task. In the case of learning of quantitativemethods at university, some students may not achieve this kind of task-orientation.

According to Gal, Ginsburg and Schau (1997), many students are not ready to embraceand function within a problem-solving-oriented learning environment in statistics

education. They experience obstacles that hinder their concentration on the task itself.The goal of the present study is to examine students orientations in a specific domain;

that of learning of quantitative methods, and also in a specific learning situation. Therewill thus be a focus in students situational orientations and domain-specific approaches tolearning of quantitative research methods.

1.2.Views, beliefs and conceptions of researchIndividuals conceptions of research

Students conceptions of the learning of research methods might be embedded in moregeneral conceptions of learning and studying. Students are shown to have differingconceptions about what learning and studying are (e.g. Marton & Slj, 1976; Entwistle &Ramsden, 1983; Lonka & Lindblom-Ylnne, 1996). According to Entwistle, McCune and

Walker (2001), conceptions of learning are derived from the cumulative effects ofprevious educational and other experiences, and so tend to be relatively stable and toinfluence, to some extent, subsequent ways of thinking and acting. Thus, in the learning ofresearch methods, students previous experiences influence their way of thinking aboutthe learning tasks, and these influence their ways of learning when attending research

methodology courses.There is a reasonable body of empirical data showing that the conceptions people holddo have implications for their learning outcomes. For example, students conceptions oflearning have been shown to be related to their study orientations, approaches to learningand study outcomes (e.g. Marton & Slj, 1976; Entwistle & Ramsden, 1983). Lonka andLindblom-Ylnne (1996) found that conceptions of learning and conceptions ofknowledge were related. They also concluded that conceptions of knowledge may guidenot only comprehension standards, but also study strategies and orientations. In the studyof Lindblom-Ylnne and Lonka (1999), it was found that students ways of interacting

with the learning environment were related to study success. Meaning-oriented

independent students succeeded best in their studies, while reproduction-oriented andexternally regulated students achieved the lowest grades. Similarly, it could be argued thatthe conceptions students hold about statistics and research methodology might have animpact on their learning of these subjects.

Ryder, Leach and Driver (1999) examined university natural science students' imagesof science. According to them, these images are particularly important because studentsactions during science learning tasks can be influenced by their ideas about the nature ofscientific knowledge and because science graduates may need to carry out tasks whichrequire an understanding of science. Similarly, in social sciences, students methodologicalchoices in course work, theses etc. might be influenced by their conceptions of researchmethodology.


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Some students may have a conception of science and research that can be classified asmythical. Scientists can be seen as a special class of people who are particularly endowed

with superior mental abilities, exceptional problem solving competence, and well-tunedscientific process skills that they use in an impartial pursuit of truth (McGinn & Roth,1999). This view is strengthened by the traditional research textbooks that give abstracted,

cookbook-like descriptions of research, and which do not provide students with anunderstanding of the process of scientific inquiry. McGinn and Roth (1999) wrote that themythical views of science and scientists have been challenged over the past two decadesby research following the traditions of sociology, anthropology, and ethnomethodology.

They conclude that scientific method is largely a myth and does not describe whatscientists actually do. Research and its products are now recognised as situationallycontingent achievements involving scientists, technicians, granting agencies, politicians,tools and instruments, local cultures, and so on. That is to say, scientific knowledgeemerges from a nexus of interacting people, agencies, materials, instruments, individualand collective goals/interests, and the histories of all these factors. Accordingly, science

education needs to look toward new educational aims that reflect the situated, contingent,and contextual nature of science, while also acknowledging the diverse range ofcommunities and locations in which science is created and used.

University students conceptions of research in general have just recently started to bestudied. Meyer, Shanahan and Laugksch (2005) have conducted a study with open-endedquestions, such as, how you would explain research? and what do you think goodresearch is? Students responses were categorised as: information gathering, discoveringthe truth, insightful exploration and discovery, analytic and systematic enquiry,incompleteness, re-examining existing knowledge, problem-based activity, and a set of

misconceptions. An inventory was constituted on the basis of the students responses, andvery similar types of dimensions were found in another sample. Thus, there seems to bevariation in students ways of understanding research.

According to Brew (2001), every conversation about research in universities, everyresearch project, and every discussion in research committees rests on the underlying ideasresearchers have concerning what research is and what researchers are doing when theycarry it out. It is assumed that researchers mostly agree about what research is, at least

within specific disciplines. Further, it is assumed that teachers of research courses knowand agree on what research is and know how to teach it. Research students are thenassumed to learn what research is without explication of the possible and varying

conceptions of research.Different conceptions of science are not only typical of students but can also be found

among professional researchers. Brew (2001) found that there was variation in howresearch is experienced by researchers. Australian researchers from many differentacademic fields were interviewed and asked to describe their views on research. Brewidentified four categories of conceptions. In the domino conception, research is viewed asseparate techniques and activities, and the goal is to synthesise these separate elements tosolve a problem or answer or open up a question. In the layer conception, hidden meaningsare sought, and research is interpreted as a process of discovering, uncovering or creating

underlying meanings. The trading conceptionemphasises products, end points, publications,grants and social networks. Research is thus understood as a kind of social market place where the exchange of products takes place. In the journey conception, the researcher


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considers personal existential issues and dilemmas. Research is thus interpreted as apersonal journey of discovery, possibly leading to a transformation. Academics may ofcourse exhibit evidence of more than one conception. Brew also found that researchersfrom any one discipline could be represented in any or all categories. These categories arehelpful for understanding why, at times, researchers or politicians referring to research do

not seem to be discussing the same thing, or are unable to communicate effectively. Theymay have different conceptions of research. Brew also suggests that this would be animportant issue to discuss in the education of postgraduates and early career researchers inorder to help them understand the different ways in which research can be conceptualised.From the above-mentioned descriptions of research, analysed from the point of view ofsociology of science (e.g. Latour, 1988), we can conclude that many researchers may haverather limited and fragmented ideas about the complex social features of their professionand of the characteristics of research as collaborative practice.

The study by Kiley and Mullins (2005) on supervisors conceptions of researchrevealed very similar conceptions of research as those found in the study by Brew (2001)

on experienced researchers conceptions. The question still remains whether a differencebetween students and supervisors conceptions of research are likely to impede studentsprogress and even completion of their degree.

Students conceptions of research do not only precede their way of taking a course onresearch methods at university. The conceptions may have also more longstanding effects,such as directing students when selecting a job, or contributing to how the future work

will be undertaken. Students may have unrealistic views of their future job, for examplethat research skills are not needed in it. Students do not always have a realistic picture oftheir future work, as shown in a comparison study on experts and novices in the domain

of education and computer science, where it was found that professionals rated the needof decision-making skills, problem-solving skills and higher order thinking skills in generalhigher than students (Tynjl, Helle & Murtonen, 2002).

Onwuegbuzie (2000, 329) found that education students perceived job competencewas not related to statistics anxiety. He concludes that this might reflect the fact thatmany statistics-anxious students tend to select careers that necessitate minimal quantitativetechniques. Thus it is possible that, providing individuals who have high levels of anxietyavoid quantitatively based professions, they will not necessarily have negative perceptionsabout their job competence. Some persons may even have positive perceptions culminating in a nonrelationship between statistics anxiety and perceived job

competence.

Cultural conceptions: The two research paradigms

Students' beliefs are often thought to arise from their own experiences, such as in thehypothesis above about bad previous experiences with mathematics, which refers tostudents' own situations that create the problems. The sources of beliefs, attitudes andexpectations can, however, be various. The educating institution, relatives, friends, or the

whole society can create and maintain beliefs that may foster or impede learning. A common belief in our society is reflected in the division into technical and

human values. In 1959, Snow (1964) gave his famous Rede Lecture at Cambridge aboutThe two cultures, where he suggested that western society had been divided into two


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poles, scientific and non-scientific. The theory is still very tenable, and it can be seen atboth individual and at societal level. At the individual level, some people may, forexample, consider the world in terms of soft and hard issues or values, of which hardissues are based on the technical and numerical approach and cannot be mastered by aperson who behaves and thinks according to the soft, humanistic approach. For example,

there may be a conception that skills in mathematics and languages are mutually exclusiveand opposite qualifications.

In the social sciences, research is often divided into technical quantitative research andhumanistic qualitative methods. Some research books even teach this. In BeginningResearch in Psychology. A practical Guide to Research Methods and Statistics, Dyer(1995, xv) states that While it is still true that the experimental method is for manyresearchers the method of first resort, many also do research by other means, includingthe soft methods such as interviews and participant observational studies. The problem

with these two poles of research in the social sciences is not only at the level ofindividuals thoughts, but has also been typical of the whole academic discipline since the

early 80s (e.g. Smith, 1997).At the beginning of the 20th century, educational researchers adopted the scientific

way to study educational questions. Questions of learning were studied in laboratoriesunder strict control, and statistical analyses were applied. Soon, however, some criticismarose and qualitative approaches started to gain advocates (Mc Kenna, 1990). Smith (1997)analyses the fragmentation of the educational research community into the qualitative andquantitative research camps. According to him, this balkanisation is a result of peopleengaging different vocabularies to tell different stories about research and the work ofresearchers. The situation has grown into what Snow (1964) describes as them having a

curious distorted image of each other.There are several papers that note that the division into two camps - qualitative andquantitative - is by no means clear. H. Becker (1996) has considered the problem of seeingqualitative epistemology as opposed to quantitative epistemology. Both kinds of researchtry to see how society works, to describe social reality, to answer specific questions aboutspecific instances of social reality. According to him, both rely on the same epistemologybut, to some extent, there has occurred a division of social sciences into two scholarlycommunities that have constituted worlds of their own, with their own languages,journals, organisations, presidents, prizes, and all the other paraphernalia of a scientificdiscipline. For these reasons, the two methodologies are also considered somehow

intrinsically different. Ttt (2000) writes about the tendency of the different camps - qualitative and

quantitative - to emphasise their own excellence by inveighing against the other. Especially with the rise of the qualitative tradition, the quantitative tradition has been used as anexample of bad research, which is not able to produce new theories but only to test theold ones. However, as Ttt (2000) puts it, both qualitative and quantitative researchmethods are empirical and both can be equally near to or far from theory. Mayer (2000)has pointed out that the division into quantitative and qualitative should not be consideredas a division into scientific and non-scientific, but that both quantitative and qualitative

can be scientific or non-scientific depending on other requirements.If scholars tend to divide themselves into two camps, it is also probable that studentsmay make a distinction between the methods. These conceptions of society and the


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science community may form students conceptions of what a good scientific method is.The culturally formed conceptions of science and human activity in general should not beomitted when studying adults conceptions. Cotner et al. (2000) interviewed doctoralstudents in education about their attitudes toward qualitative research. They found thatstudents described varying degrees of sympathy and interest in qualitative research even

before taking their first methodology class in their doctoral programme. Some of thestudents said that it never crossed my mind to do anything but a qualitative dissertationand Im a more qualitative person in general (Cotner et al., 2000, 3). This shows thateven students can have widely generalised conceptions about research that may guide theirchoices and decisions.

According to Johnson and Onwuegbuzie (2004), the quantitative versus qualitativedebate has been so divisive that some graduate students who graduate from educationalinstitutions with an aspiration to gain employment in the world of academia or researchare left with the impression that they have to pledge allegiance to one research school ofthought or the other. The curricular structures and the literature have further emphasised

this division by separating these contents into separate courses and books. Only recentlyhas this division been challenged in the literature, when Tashakkori and Teddlie (2003)published the Handbook of Mixed Methods in Social and Behavioral Research. Resting onthe groundings of mixed methods methodology, Onwuegbuzie and Leech (2005) havesuggested ways to teach qualitative and quantitative methods together by eliminatingstatistics courses from curricula and replacing these with research methodology courses atdifferent levels that simultaneously teach students both quantitative and qualitativetechniques.

If students do choose their side between the technical and humanistic views, in this

case between quantitative and qualitative, there might also be other sociocultural factorsaffecting the result apart from just the formal, publicly expressed division between these views. Students behaviour in the classroom can be seen as a function of the interplaybetween who they are (their identity), and the specific classroom context (Opt Eynde, DeCorte & Verschaffel, 2001), which, in the case of research methods, would include thepolarised view of research methods. Hannover and Kessels (2004) have studied highschool students dislike of mathematics and science from a social psychology perspective.

They suggest a prototype theory, according to which people compare themselves and afavourite or a least-liked prototype. These prototypes may also be culturally formed andtaught. Hannover and Kessels (2004, 54) give an example of this self-to-prototype matching

approach: Consider a person who wants to buy a new piece of clothing on the occasion of adinner party invitation. While he or she is flicking through a fashion magazine and lookingat various outfits, he or she may imagine the prototypical buyer for each of the pieces ofclothing. The individuals self-definition serves as a reference point against which thefeatures of these prototypical persons can be compared. The person is therefore expectedto buy the piece of clothing which most closely reflects the image he or she has of himselfor herself, i.e. for which he or she found the strongest similarity or overlap between theprototypical person wearing that outfit and his or her own self-image.

On the basis of their findings, Hannover and Kessels (2004) suggest that high school

students do not like mathematics and science because the prototypes they have for peoplewho like these subjects is not what they want to be like themselves. Whereas prototypicalmaths-liking students were considered socially incompetent, isolated and not creative,


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prototypical German and English language-liking students were seen in a positive light. This may be the situation in quantitative methods courses too: a student may have aprototypical image of a student who likes quantitative methods or of a worker using theseskills in working life, and he or she does not want to be like that. These views may also beconnected to some other unrealistic views or conceptions, for example, about the future

job. For example, a psychology university student may imagine a favourite prototype of apsychologist whose work is solving peoples problems by talking with them, as seen intelevision series, and a least-liked prototype of a psychologist who analyses data with acomputer, using statistics.

According to Hannover and Kessels (2004), the prototypes may also result from thescript of instruction that guides lessons. If a subject is taught in narrow-focused classwork,like mathematics and science lessons often are, it may feel more dull than, for example,language classes that utilise group work, students presentations and discussions aboutdifferent ways of solving a problem.

Another type of theory on the socioconstructive forming of conceptions is presented

by Orr (1990), who suggests that people tell stories about their work to build theiridentity, and to show that they are competent members of the community. In the same

way, it could be assumed that people in the field of research set standards for their workby telling stories about it and emphasising the points that they think are relevant. In this

way, a general view is created and it is also likely to be taught to the new members of thecommunity. In addition to teachers, older students can also socialise new students theprevailing beliefs. If teachers and older students tell new students stories about howdifficult statistics is to learn, the new students are probably more sensitive to similarexperiences themselves, and tell the next students the same story. In this way, the dislike

of statistics can become as an accepted secret. Both students and staff know that it is aproblem, but nothing is done to solve the situation.In many areas of human knowledge, people do not want to admit that they do not

know or cannot do something. In the domain of statistics, there seems to be noembarrassment about saying that one does not understand anything about statistics.Paulos (1991) writes about the same phenomenon in mathematics in his book calledInnumeracy, i.e. that some people are even proud of being ignorant of or bad atmathematics.

1.3.Cognitive processes in the learning of research

The difficulty of learning of statistics and research methodology cannot be explained onlyby emotional and conceptual factors. There are probably some features in the domain ofresearch that make the learning of it difficult for many people. Research methodologycontains elements that make the learning of it cognitively challenging, such as abstractnessand complexity. The rules and conventions of research in society have been developedover a long period of time, and these have raised the level of abstractness of researchmethodology (cf. Lakoff & Nez, 1997). When more and more concepts becomeinterrelated, knowledge becomes elevated to a higher level of abstraction (Broers, 2002).

In learning this takes time and the way is not always easy.


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According to Watts (1991), a major difficulty that confounds beginning students andinhibits the learning of statistics is that the important fundamental concepts of statisticsare quintessentially abstract. The concepts and principles of statistics, such as probability,are not used in everyday life and they can be hard for some students to understand.

Anderson, Pirolli & Farrell (1988) have explored how students learn to programme

recursive functions, a task that also has been found to be difficult for university studentsto learn. They concluded that learning recursive programming is difficult because it is anunfamiliar activity, with hidden complexities, that must be learned in an unfamiliar anddifficult domain. Similarly, in the domain of methodology, students face many conceptsthat they have never heard of, and the process of scientific research may not be familiar.For example, principles of scientific research and statistical inference can be far fromstudents everyday activities and inference, research activities in certain domains are verycomplex, and the connection between theory and practice can be difficult to see. Thedevelopment of statistical knowledge also seems to demand the adoption of rules andideas that to many are counterintuitive and therefore difficult to master (Broers, 2001).

Research may also appear abstract because of some of the tools it uses. For example,statistical formulas require skills in the formal symbol system and the language of statistics,

which can be hard for students to understand. Onwuegbuzie (1997), in a study concerninguniversity students anxiety in research proposal writing, found that some students had afear of statistical language. In particular, formulas, symbols, notation, and the terminologyincreased the levels of statistics anxiety. The students equated learning statistics withlearning another language. In addition to the formal symbol system and the language ofstatistics, the teachers way of talking about statistics may not be familiar to students.Broers (2002) found that psychology students remembered verbal propositions

concerning statistics more easily than abstract facts. He proposes that this is because mostpsychology students do not tend to think mathematically but in terms of concrete verbaltheories of reality. If statistics is taught by a person who thinks mathematically, theremight be a problem with mutual understanding. For example, if a statistician tries to teachsome statistical concepts by using statistical language, it may be inaccessible to students. Inaddition to the statistics language, teachers may use a specific type of language typical ofthe scientific community. According to McGinn and Roth (1999), scientific communitiesare characterised by their specific forms of discourse and disciplines have their own

vocabularies. These specific vocabularies may further widen the gap between students andteachers understanding.

The knowledge on research that students read from books and study in courses is noteasy to understand or easily transmitted into practice. Methodological expertise requires

vast amounts of conceptual knowledge (knowing what), although the research processin itself requires procedural knowledge (knowing how) (e.g. Hiebert & Lefevre, 1986).Students may find it hard to convert the abstract conceptual knowledge into theprocedural knowledge needed to conduct research and to truly understand researchactivity. Broers (2002) found that undergraduate psychology students experienceddifficulties in solving a statistical problem, although they possessed the relevant factualknowledge about it. The knowledge of facts, terms and procedures should be integrated

into a network of interrelations, i.e. conceptual understanding, before the solving of anabstract problem is possible. (Broers, 2001 & 2002).


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According to Sweller and Chandler (1994), some material can be difficult to learnbecause of the heavy cognitive load. The cognitive load associated with the material to belearned is strongly related to the extent to which the elements of that material interact witheach other. The interactions between the various elements may provide the whole point of

what must be learned, so the elements of the task cannot be learned in isolation because

they interact with each other. Under these circumstances, learning is not just a function ofthe number of elements that must be learned but also a function of the elements that mustbe learned simultaneously. In the case of research methodology, the elements interactextensively with each other, and they cannot be learned without understanding the wholesystem, which burdens the working memory and thus cognitive capacity. In addition tothe abstract domain of research methodology, students prior knowledge may not be atthe level that teachers assume it to be. This increases the amount of content to be learned,and further enhances the cognitive load.

Lehtinen and Rui (1995) suggest that problems in the learning of researchmethodology appear partly because of the complexity of the domain, i.e. methodological

knowledge includes several challenging properties for the learner: the sub domains arehighly abstract and partly controversial, the links between them are abstract and basedpartly on structural analogies, and comprehension of the domain requires that theconcrete procedures should be understood within the framework of the whole complexsystem. Onwuegbuzie (1997) found that some students experience statistics anxiety, forexample, when attempting to utilize statistical principles in order to understand the resultssection of a quantitative research article, or to select an appropriate statistical analysis fortheir research questions or hypotheses. These actions require skills to handle the complex

whole of the research process and principles, i.e. the anxiety expressed in these situations

may be caused by the complexity of the material.Skills and knowledge in quantitative methods cannot be learnt without thedevelopment of scientific thinking. What, then, is scientific thinking? Klahr (2000, 2)

writes that it is a form of human thinking, and the nature of human thinking is one of theBig Questions along with the nature of matter, the origin of the universe, and the natureof life, so it cannot be fully answered. However, he does offer some descriptions by which

we may differentiate scientific thinking from other forms of thinking, for example, thatscientific thinking has enhanced our ability to understand, predict, and control the naturalforces that shape our world. The argument of Klahr (2000, 4) is that the processes thatsupport creative scientific discovery are not substantially different from those found in

more commonplace thinking; he quotes Einstein who has said that the whole of scienceis nothing more than a refinement of everyday thinking.

From the perspective of a university student, scientific thinking requires certain formsof reasoning and problem-solving skills. The knowledge contains both very abstract and

very practical elements that set their own challenge to learning. The learner also needsmetacognitive skills to fully understand the topic. Kallio (1998) refers to Piagetsdevelopmental theory and concludes that formal reasoning and reflection on it can beclaimed to be the highest developmental levels of scientific reasoning. She studied thetraining of university students scientific reasoning skills. She found that causal scientific

thinking could be taught to students, and that the performance was sustained at the 16- week delayed post-test. The best results were gained by teaching metastrategiessimultaneously with causal thinking. She concludes that it is important to teach


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metastrategic thinking skills. This can be done, for example, by presenting a structure ofthe domain with a figure, and by comparing and connecting the similarities anddifferences of the contents. Metacognitive skills are also emphasised by Kuhn, Amsel andOLoughlin (1988, 228), who have studied the development of scientific thinking skillsamong children. They suggest that there is an important difference between thinking with

theories from thinkingabouttheories, the latter enabling awareness and control over thetheories.

Learning and becoming skilled in some domain have been largely studied under theconcept of expertise. Classical studies on expertise (e.g. Chi, Glaser & Farr, 1988) wereconducted mostly on individual skills, i.e. how individuals knowledge and acting with thetask set the grounding for succeeding in the work. Recent theories emphasise the role ofthe environment and other people. For example, Hakkarainen, Palonen, Paavola andLehtinen (2004, p. 60) propose a model of growing up to an expert community. First, anovice needs scaffolding, the experts personal coaching and guidance adjusted to thenovices developing skills. Then, through joint working under the experts guidance and

responsibility the novice gradually becomes a participant in a community of practice andbecomes integrated into an expert culture.

On the basis of these models, it does not seem reasonable to introduce the verycomplex and abstract domain of quantitative research methods to students through givinginstruction only in, for example, statistical tests features, but it is necessary to try tointroduce the whole process of research to students. On the other hand, the questionshould be asked that what kind of skills should university graduates have, and morespecifically, what skills should bachelors, masters and doctors have. Th

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